We proposed a hybrid approach using the computational and statistical resources of the Bayesian Networks to learn a network structure from a data set using 4 different algorithms and the robustness of the statistical methods present in the Structural Equation Modeling to check the goodness of fit from model over data. We built an intermediate algorithm to join the features of 'bnlearn' and 'lavaan' R packages. The Bayesian Networks structure learning algorithms used were 'HillClimbing', 'MaxMin HillClimbing', 'Restricted Maximization' and 'Tabu Search'.
Package details 


Author  Elias Carvalho, Joao R N Vissoci, Luciano Andrade, Emerson P Cabrera, Julio C Nievola 
Maintainer  Elias Carvalho <[email protected]> 
License  GPL3 
Version  0.2.0 
URL  https://sites.google.com/site/bnparp/ 
Package repository  View on CRAN 
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